The invention relates to image acquisition devices such as graphics arts scanners, and more particularly to modeling the spectral behavior of an image acquisition device.
Scanners produce digital output values to represent the spectral reflectance of an input sample. A typical scanner illuminates a reflective target using a light source. The scanner=s sensors integrate light that reflects off the sample and passes through a set of spectrally selective filters. The integrated products may be modified by electronics and software to obtain digital output values.
The digital values generated by a scanner are dependent upon the filters and illuminant of the particular scanner and so are not device-independent values. To use the results of scanning a sample on a different device, device-independent values are desirable. Device-independent values include the spectral reflectance of the sample or other derived colorimetric values, such as CIE XYZ or CIE L*a*b* values.
In addition, different reflectance spectra can be perceived by the eye as the same color. Similarly, different spectra can produce the same digital values. This effect is called xe2x80x9cmetamerism.xe2x80x9d Metameric samples can produce different colorimetric response values when viewed or scanned under different viewing conditions. As a result, in modeling scanners, many different reflectance spectra can produce the same RGB values. To determine a more accurate estimate of a reflectance spectrum, it is desirable to limit the candidates to avoid metameric matches.
The invention provides methods and apparatus implementing a technique for modeling spectral characteristics of an image acquisition device. In one implementation, a computer system predicts the spectral reflectance or transmittance of a sample scanned by an image acquisition device, such as a graphics arts scanner, by modeling the device. The sample is scanned by a scanner. The computer system searches for media coordinates in a colorant space corresponding to the sample. The media coordinates correspond to an estimated spectrum in a media model. The estimated spectrum generates estimated digital values through a forward model of the scanner. The estimated digital values are compared to target digital values generated by the scanner to calculate an error value. The computer system repeats this process until a desired stopping criterion or criteria are met. The estimated spectrum corresponding to the final estimated digital values represents the reflectance spectrum of the sample as scanned by the scanner.
In general, in one aspect, the technique includes: (a) estimating media coordinates in a colorant space of a sample scanned by an image acquisition device, where the estimated media coordinates correspond to target digital values produced by the image acquisition device when scanning the sample; (b) converting the estimated media coordinates to an estimated spectrum using a media model corresponding to the sample; (c) estimating digital values by supplying the estimated spectrum to a forward model which models the image acquisition device; (d) identifying an error between the estimated digital values and the target digital values; and (e) if a stopping criterion has not been met, searching the colorant space for media coordinates according to the error using a constrained minimization technique and repeating steps (b) through (e) until the stopping criterion has been met.
Advantages that may be seen in implementations of the invention include one or more of the following: the estimated spectrum is a device-independent and viewing-condition-independent approximation of the spectral reflectance of the sample; the model of the scanner can be used to predict the scanner""s output without actually scanning which is useful for experimental purposes; in addition, the media model and the scanner model are independent and so the media model may be changed while still preserving the colorimetric characterization of the scanner. Furthermore, fewer models are required when the media and scanner characterizations are independent characterizations than when the media and scanner characterizations are dependent. For example, if there are M media characterizations and N scanner characterizations, when the characterizations are independent, the total number of models is M+N. When the characterizations are dependent, the total number of models is Mxc3x97N.